SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques
| Autor: | Ludovica De Gregorio, Giovanni Cuozzo, Francesca Cigna, Claudia Notarnicola, Emanuele Santi, Deodato Tapete, Alexander Jacob, Simone Pettinato, Simonetta Paloscia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Synthetic aperture radar
010504 meteorology & atmospheric sciences Artificial neural network Backscatter Mie scattering 0211 other engineering and technologies 02 engineering and technology Snow 01 natural sciences Support vector machine Radiative transfer Satellite Geology 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science. |
| DOI: | 10.23919/ursigass49373.2020.9232247 |
| Popis: | The potential of satellite Synthetic Aperture Radar (SAR) sensors for Snow Water Equivalent (SWE) retrieval in Alpine areas is assessed in this study. X-band HHpolarized SAR backscatter from 2012-2015 images acquired by the COSMO-SkyMed constellation over the South Tyrol province in northern Italy is compared with SWE in-situ measurements and nivo-meteorological station records. The resulting relationship is compared with simulations based on the Dense Media Radiative Transfer – Quasi Mie Scattering (DMRT – QMS) model. Artificial Neural Networks (ANN) and Support Vector Regression (SVR) machine learning techniques are trained and used for SWE retrieval from COSMO-SkyMed data. Good accuracy and small computational cost are observed for both ANN and SVR. The resulting SWE maps agree with snow conditions measured in-situ. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|